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Opazovanje razvoje mest s časovno vrsto satelitskih posnetkov : diplomska naloga
ID Kuhar, Blaž (Author), ID Oštir, Krištof (Mentor) More about this mentor... This link opens in a new window, ID Foški, Mojca (Co-mentor)

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Abstract
Satelitski posnetki Landsat so namenjeni opazovanju kopnih površin, imajo dolgotrajen arhiv ter so prosto dostopni javnosti, zato so še posebej primerni za razne časovne analize. V diplomski nalogi smo na podlagi posnetkov pridobljenih med leti 1987 in 2017 izvedli časovno analizo sprememb pozidanega površja na območju Ljubljane in Houstona. Uporabljena je metoda nadzorovane klasifikacije po algoritmu največje verjetnosti, katere rezultat je po šest klasifikacij za vsako območje. Klasifikacije so ovrednotene na podlagi stotih naključnih točk. Na podlagi klasifikacij so izdelane karte sprememb, ki prikazujejo le spremembe razreda pozidano glede na ostale razrede (gozd, voda in kmetijske površine). Predstavljena je analiza večanja pozidanih območij ter primerjava le teh za obe obravnavani območji.

Language:Slovenian
Keywords:satelitski posnetki, Landsat, klasifikacija, časovna analiza
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FGG - Faculty of Civil and Geodetic Engineering
Publisher:[B. Kuhar]
Year:2018
PID:20.500.12556/RUL-103331 This link opens in a new window
UDC:528.7/.8(043.2)
COBISS.SI-ID:8548193 This link opens in a new window
Publication date in RUL:16.09.2018
Views:4073
Downloads:393
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Secondary language

Language:English
Title:Observing the development of cities with a time series of satellite imagery : graduation thesis
Abstract:
Landsat satellite images of Landsat are used for land observation, they have a long-term large archive and are freely available, therefore they are especially usefull for time-series. In this thesis we carried out a time-series of built-up areas in Ljubljana and Houston, based on Landsat images acquired between the years of 1987 and 2017. We used the supervised classification with a maximum likelihood classification algorithm, whose results are six classifications for each area. Based on one hundred random points, we evaluated each classification. By comparing classifications we made change maps, which show changes in the built class, according to other classes (forest, water and agricultural land). An analysis of the of enlargement built-up areas is presented. A comparison of the study areas is described.

Keywords:satellite images, Landsat, classification, time analysis

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